<html>
   <head>
      <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
   
      <link rel="stylesheet" href="./../helpwin.css">
      <title>MATLAB File Help: prtScoreRocBayesianBootstrapNfa</title>
   </head>
   <body>
      <!--Single-page help-->
      <table border="0" cellspacing="0" width="100%">
         <tr class="subheader">
            <td class="headertitle">MATLAB File Help: prtScoreRocBayesianBootstrapNfa</td>
            
            
         </tr>
      </table>
      <div class="title">prtScoreRocBayesianBootstrapNfa</div>
      <div class="helptext"><pre><!--helptext -->  <span class="helptopic">prtScoreRocBayesianBootstrapNfa</span>   Generate a reciever operator characteristic curve with Bayesian Boostrapping
 
    <span class="helptopic">prtScoreRocBayesianBootstrapNfa</span>(DECSTATS,LABELS) plots the receiver
    operator characteristic curve for the decision statistics DECSTATS and
    the corresponding labels LABELS. DECSTATS must be a Nx1 vector of
    decision statistics. LABELS must be a Nx1 vector of binary class
    labels. This behavior is the same as prtScoreRocBayesianBootstrap. The
    only difference is when output arguments are requested (see below).
 
    <span class="helptopic">prtScoreRocBayesianBootstrapNfa</span> performs Bayesian boot strap sampling
    of an ROC curve and generates the 100*(1-alpha) percent percentile
    uniform credible band. The default alpha is .05, corresponding to a 95%
    credible band. This is done following the methodology in:
 
    Non-parametric estimation of ROC curve J. Gu, S. Ghosal, and A. Roy
    Statistics in Medicine, Vol. 27, 5407&#x97;5420, 2008.
    <a href="http://www4.stat.ncsu.edu/~ghosal/papers/ROCBB.pdf">http://www4.stat.ncsu.edu/~ghosal/papers/ROCBB.pdf</a>
 
    <span class="helptopic">prtScoreRocBayesianBootstrapNfa</span>(DECSTATS,LABELS,
    NBOOTSAMP) Specifies the nummber of boostrap samples NBOOTSAMP. The
    default value is 1000.
 
    <span class="helptopic">prtScoreRocBayesianBootstrapNfa</span>(DECSTATS,LABELS, [],
    NPFSAMP) Specfies the number of samples of probability of false alarm
    at with which to sample the ROC curve. The default is 500.
 
    <span class="helptopic">prtScoreRocBayesianBootstrapNfa</span>(DECSTATS,LABELS, [],
    [], ALPHA) Specifies ALPHA, the size of the credible interval
    100*(1-alpha). The default is 0.05, corresponding to a 95% credible
    band
 
    [NFA, PDMEAN,PDCONFREGION, BOOTSTRAPPEDPDS] =
    <span class="helptopic">prtScoreRocBayesianBootstrapNfa</span>(...) outputs NFA, the
    number of false alarms at which the bootstrapped ROC curves are
    evaluated. PDMEAN, the mean of the bootstrapped ROC curves,
    PDCONFREGION, the 100*(1-alpha) percent percentile uniform credible
    band reported as the upper and lower Pd curves. BOOTSTRAPPEDPDS, all
    samples of the bootstrapped ROC curves
 
     Example:     
     TestDataSet = prtDataGenBimodal;       % Create some test and
     TrainingDataSet = prtDataGenBimodal;   % training data
     classifier = prtClassSvm;             % Create a classifier
     classifier = classifier.train(TrainingDataSet);    % Train
     classified = run(classifier, TestDataSet);     
     % Find the number of false alarms at the corresponding PD.
     [nf, pd]= <span class="helptopic">prtScoreRocBayesianBootstrapNfa</span>(classified.getX, TestDataSet.getY, [],[],.2);</pre></div><!--after help --><!--seeAlso--><div class="footerlinktitle">See also</div><div class="footerlink"> <a href="./prtScoreConfusionMatrix.html">prtScoreConfusionMatrix</a>, <a href="./prtScoreRmse.html">prtScoreRmse</a>, <a href="./prtScoreRoc.html">prtScoreRoc</a>,
    <a href="./prtScoreRoc.html">prtScoreRoc</a>, <a href="./prtScorePercentCorrect.html">prtScorePercentCorrect</a>
</div>
   </body>
</html>